Systems and methods for equipment maintenance

ABSTRACT

Disclosed are systems and methods for an industry-wide and predictive approach to maintenance of commercial equipment. In one embodiment, multiple instances of a frontend infrastructure can be deployed to various sites where one or more physical parameters of industrial equipment are monitored with wireless sensors and routed to a backend infrastructure. The backend infrastructure can process the sensor data received from the multiple sites and generate predictive maintenance notifications.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a U.S. Provisional Application No. 62/982,005 filedon Feb. 26, 2020, the contents of which is incorporated herein byreference in its entirety specification.

BACKGROUND Field

This invention relates generally to the field of commercial orindustrial equipment maintenance, and more particularly to systems andtechniques to provide industry-wide, predictive maintenance.

Description of the Related Art

Existing methods of providing diagnostics and maintenance for industrialand commercial equipment rely largely on human technicians and ononboard diagnostics that tend to treat each equipment in isolation.Often a technician tradesman's knowledge and experience of an equipmentcan be limited to the geographical area his company operates in and/orthe customers his company services. While the advent of internet andonline forums have contributed to sharing repair knowledge among humanoperators and technicians, there has not been a holistic approach todiagnostics and maintenance that can impart industry-wide benefits interms of improving the efficiency of equipment and better maintenance ofequipment.

Furthermore, existing technology in the area of equipment failurediagnostics and maintenance can be reactive rather than proactive. Forexample, sometimes the conditions and circumstances of an equipmentfailure are not well documented and shared. Therefore, operators of theequipment can be unaware of potential equipment failure. Importantindustries can suffer economic harm when existing diagnostics ormaintenance techniques cannot be reliably applied to determine equipmentfailure before they occur. Consequently, there is a need for improveddiagnostics and maintenance systems and techniques, which can provide aholistic view of equipment in an industry and furthermore providepredictive maintenance functionality.

SUMMARY

In one aspect a method is disclosed. The method includes: receiving aprofile of an equipment from an operator of the equipment, wherein theprofile comprises one or more physical parameters of the equipment to bemonitored and normal ranges of the physical parameters; monitoring, withone or more sensors, the one or more physical parameters of theequipment; transmitting the physical parameter values to a backendserver; determining if the physical parameter values are outside thenormal range and generating a notification; determining one or morepatterns in the physical parameter values over a period of time; andgenerating a notification if the one or more patterns are indicative ofan anomaly in operation of the equipment.

In some embodiments, the one or more patterns indicative of an anomalycomprise the one or more parameter values approaching a range outsidethe normal range over a period of time, but not exceeding the normalrange over the period of time.

In another embodiment, the one or more patterns indicative of an anomalyare determined via one or more machine learning algorithms based onmonitored parameter values of a plurality of equipment over a period oftime.

In one embodiment, the method further includes: detecting one or moreequipment-wide patterns indicative of anomaly in operation, the one ormore equipment-wide patterns shared among a plurality of same or similarequipment; generating a notification for some or all of the same orsimilar equipment.

In some embodiments, the method further includes: generating a set ofconditions based at least partly on the one or more patterns, whereinthe set of conditions correlate with the anomaly; generating anequipment repair profile corresponding to the anomaly, along with amapping of the equipment to the equipment repair profile; detecting thepresence of the set of conditions in a second equipment; transmittingthe equipment repair profile to an operator of the second equipment.

In one embodiment, the method further includes transmitting theequipment repair profile to other operators having equipment same orsimilar to the second equipment.

In another embodiment, the method further includes: generating equipmentrepair profiles of a plurality of equipment, corresponding to anomaliesdetected based on the one or more patterns in the physical parameters;based at least partly on the equipment repair profiles, detecting a setof conditions shared amongst the plurality of equipment having thedetected anomalies; generating a second repair profile corresponding tothe set of conditions and the detected anomalies corresponding to theset of conditions; detecting presence of the set of conditions in asecond plurality of equipment; and transmitting the second repairprofile to one or more operators of the second plurality of equipment.

In some embodiments, the set of conditions comprise one or more of; abrand of the equipment, or part identification of a previously repairedor replaced part, and a duration of time after which the part neededrepair or replacement.

In another embodiment, the physical parameters comprise one or more oftemperature, vibration, electrical current and power drawn.

In one embodiment, the method further includes: transmitting a sensorconfiguration signal from the backend server to the one or more sensors,wherein the sensor configuration signal is at least partly based on theequipment profile.

In another aspect, a non-transitory computer storage is disclosed. Thenon-transitory computer storage stores executable program instructionsthat, when executed by one or more computing devices, configure the oneor more computing devices to perform operations including: receiving aprofile of an equipment from an operator of the equipment, wherein theprofile comprises one or more physical parameters of the equipment to bemonitored and normal ranges of the physical parameters; monitoring, withone or more sensors, the one or more physical parameters of theequipment; transmitting the physical parameter values to a backendserver; determining if the physical parameter values are outside thenormal range and generating a notification; determining one or morepatterns in the physical parameter values over a period of time; andgenerating a notification if the one or more patterns are indicative ofan anomaly in operation of the equipment.

In one embodiment, the one or more patterns indicative of an anomalycomprise the one or more parameter values approaching a range outsidethe normal range over a period of time, but not exceeding the normalrange over the period of time.

In some embodiments, wherein the one or more patterns indicative of ananomaly are determined via one or more machine learning algorithms basedon monitored parameter values of a plurality of equipment over a periodof time.

In one embodiment, the operations further includes: detecting one ormore equipment-wide patterns indicative of anomaly in operation, the oneor more equipment-wide patterns shared among a plurality of same orsimilar equipment; generating a notification for some or all of the sameor similar equipment.

In some embodiments, the operations further include: generating a set ofconditions based at least partly on the one or more patterns, whereinthe set of conditions correlate with the anomaly; generating anequipment repair profile corresponding to the anomaly, along with amapping of the equipment to the equipment repair profile; detecting thepresence of the set of conditions in a second equipment; andtransmitting the equipment repair profile to an operator of the secondequipment.

In one embodiment, the operations further includes: transmitting theequipment repair profile to other operators having equipment same orsimilar to the second equipment.

In another embodiment, the operations further include: generatingequipment repair profiles of a plurality of equipment, corresponding toanomalies detected based on the one or more patterns in the physicalparameters; based at least partly on the equipment repair profiles,detecting a set of conditions shared amongst the plurality of equipmenthaving the detected anomalies; generating a second repair profilecorresponding to the set of conditions and the detected anomaliescorresponding to the set of conditions; detecting presence of the set ofconditions in a second plurality of equipment; and transmitting thesecond repair profile to one or more operators of the second pluralityof equipment.

In some embodiments, the set of conditions comprise one or more of abrand of the equipment, or part identification of a previously repairedor replaced part, and a duration of time after which the part neededrepair or replacement.

In one embodiment, the physical parameters comprise one or more oftemperature, vibration, electrical current and power drawn.

In some embodiments, the operations further include: transmitting asensor configuration signal from the backend server to the one or moresensors, wherein the sensor configuration signal is at least partlybased on the equipment profile.

BRIEF DESCRIPTION OF THE DRAWINGS

These drawings and the associated description herein are provided toillustrate specific embodiments of the invention and are not intended tobe limiting.

FIG. 1 illustrates a maintenance system according to an embodiment.

FIG. 2 illustrates a system for converting raw sensor data to a formatmore useful for consumption by a human operator of an equipmentmaintained by the embodiment of FIG. 1.

FIG. 3 illustrates a flowchart of a method of providing automatedmaintenance using the embodiment of FIGS. 1 and 2.

FIG. 4 illustrates a flowchart of a method, which can be used incombination with the embodiment of FIG. 3 to improve the operations ofthe embodiment of FIG. 1.

FIG. 5 illustrates a diagram of an artificial intelligence (AI)technique, which can be used for detecting primary or secondary patternsindicative of normal or abnormal equipment operation.

FIG. 6 illustrates a flowchart of a method of using artificialintelligence (AI) techniques for detecting patterns of sensor dataindicative of device failure.

DETAILED DESCRIPTION

The following detailed description of certain embodiments presentsvarious descriptions of specific embodiments of the invention. However,the invention can be embodied in a multitude of different ways asdefined and covered by the claims. In this description, reference ismade to the drawings where like reference numerals may indicateidentical or functionally similar elements.

Unless defined otherwise, all terms used herein have the same meaning asare commonly understood by one of skill in the art to which thisinvention belongs. All patents, patent applications and publicationsreferred to throughout the disclosure herein are incorporated byreference in their entirety. In the event that there is a plurality ofdefinitions for a term herein, those in this section prevail. When theterms “one”, “a” or “an” are used in the disclosure, they mean “at leastone” or “one or more”, unless otherwise indicated.

Industrial and commercial equipment are important parts of the economy.Often this equipment relies on periodic inspection by human techniciansto detect and perform maintenance and/or repairs. Sometimes, suchequipment can include or are retrofitted with onboard diagnostic toolsto assist human technicians in diagnostics, maintenance or repair. Inmany cases, however, important maintenance or repair may be delayed,postponed or not performed until an equipment failure disrupts thenormal operation of the equipment. Additionally, equipment maintenanceis generally performed for devices in isolation and technologicalinsights which can be gained from individual repairs and extrapolatedacross same or similar equipment in an industry are routinely lost.

In some cases, human technicians can accumulate and transfer theirknowledge of a particular brand of equipment and device and extrapolatefrom that knowledge to better perform repair or maintenance on other orsimilar devices. For example, the technician may be aware of a commonfailure in a particular brand of industrial refrigerator from his priorrepair experiences. However, such knowledge can be limited and notshared among the industry at large. Furthermore, there is noinfrastructure to collect and learn from prior repair or maintenancework of a device or industrial equipment, in a manner that the data caninform industry approaches to repair, maintenance and improvingefficiency. Such data can provide technological solutions for predictivemaintenance actions that can vastly improve the life cycle ofindustrial, commercial or consumer products by triggering timely andtargeted maintenance or repair.

Sensors can be used to monitor critical device parameters and reportanomalies to repair or maintenance technicians. However, sensors andsensor data in the context of diagnostics and maintenance are used inisolation. The described embodiments provide infrastructure and methodsof operation of diagnostics and maintenance systems, where sensorreadings are used both individually and collectively across an industryto provide improved diagnostics and maintenance.

FIG. 1 illustrates a maintenance system 100 according to an embodiment.System 100 can include frontend infrastructure 102 and backendinfrastructure 104. Multiple instances of the frontend infrastructure102 can be deployed at various sites, where the system 100 can provideautomated or augmented diagnostics and maintenance for one or moreequipment 101. In one embodiment, the frontend infrastructure 102 can beimplemented with products provided by Monnit of Salt Lake City, Utah(801-561-5555). Example Monnit products that can be used in frontendinfrastructure 102 can include sensors, gateways, sensor-side servers,and software applications that provide operations for these components.The frontend infrastructure 102 can include one or more sensors 106 ofmeasuring various maintenance and/or diagnostics parameters, gateway108, sensor-side server 110, and application software that provideinput/output for the frontend infrastructure 102, as well as generalsoftware management of the hardware of the frontend infrastructure 102.In some embodiments, the gateway 108 and/or the sensor-side 110 may beoptional and their functionality can be integrated in the sensors 106,or in one or more integrated server/gateway component, where the sensors106, and/or the integrated server/gateway components provide the rawsensor data of the frontend infrastructure 102 to the backendinfrastructure 104.

Sensors 106 can include a variety of sensors, which can measure variousdiagnostics and/or maintenance parameters, depending on the applicationand the industry in which the system 100 is deployed. For example, thefrontend infrastructure 102 can be deployed in food industryapplications, where sensors 106 can be temperature sensors for equipment101 (e.g., refrigerators). Several industries can also be interested inand might operate equipment that maintain a preferred temperature in acontrolled environment. Besides refrigerators in the food industry,storage industry, heating, ventilation and air conditioning (HVAC)industry have an interest in their equipment maintaining a desirablerange of temperatures. Persons of ordinary skill in the art can envisionother industries within which the system 100 can be used.

Sensors 106 can also be current and/or power sensors in, for example,manufacturing applications, where amount of drawn current and/or powerconsumption of manufacturing equipment is of interest both formaintenance purposes and for improving efficiency. When infrastructure102 is deployed for those applications, the sensors 106 can be currentand/or power sensors, measuring parameters and parameter values thatdirectly or indirectly indicate current drawn and/or power consumption.As an example, lack of drawn current from the equipment 101 can indicatethat the equipment 101 has gone offline, potentially due to a partfailure, jam incident or other reasons and the system 100 can generate anotification to order maintenance for the equipment 101. In otherapplications, sensors 106 can include vibration sensors that can measurevibration in equipment 101, where increased vibration can be indicativeof an upcoming need for maintenance.

Sensors 106 can have wired or wireless communication with other parts ofthe system 100. For example, the sensors 106 can wirelessly communicatetheir sensor reading to the gateway 108. Similarly, the gateway 108 cancommunicate via wired or wireless communication protocol with thesensor-side server 110. In one embodiment, the sensors use radiofrequency (RF) signals to communicate sensor readings to the gateway 108and the gateway 108 can communicate via TCP/IP protocol with thesensor-side server 110. Other wired or wireless communication protocolsbetween components of the system 100 can also be implemented.

The sensors 106 can include an identifier system, such as a barcode, aquick response (QR) code. The identifier system, such as a barcode or QRcode can be scanned at the time of deploying the sensors 106 via ascanner device in wireless or wired communication with the sensor-sidesever 110 or directly with the backend server 112. The backend server112 can receive the sensor identifier and associate the sensor 106 withthe equipment 101 for which the sensor 106 is deployed.

The sensor-side server 110 can route the sensor readings to a backendserver 112 in the backend infrastructure 104. Sensor data received fromthe frontend infrastructure 102 can be in a format that is notdecipherable or useful for an operator of the equipment 101. The system100 can generate the UI client 114 and feed it with processed data in aformat that is useful for an operator of the equipment 101 to improveits diagnostic and maintenance functions. Raw frontend infrastructuredata from sensors 106 can be received at the backend infrastructure 104in a variety of formats, including, for example, as JavaScript ObjectNotation (JSON) files. The backend server 112 can store raw frontenddata and/or processed data in a database 116. In one embodiment, thedatabase 116 can be implemented via Postgres SQL. The UI client 114 canbe generated in a variety formats, for example, as a website applicationinterface, desktop application interface, mobile computing deviceinterface (e.g., generated as a display on a screen of a laptop, tablet,watch, or special-purpose field computers designed and optimized forseamless interfacing with the backend server 112).

Data received from the frontend infrastructure 102 can also includesuperfluous details or data that have less value and relevance in termsof maintenance and improved efficiency of the equipment 101. The backendserver 112 can detect and eliminate superfluous data and retain sensorreadings that have applicability to maintenance and improved operationof the equipment 101. Example superfluous data that might exist in theoutput of the frontend infrastructure 102 can include extra characters,encoding data, third-party application and graphing data, deviceidentifiers unrelated to the equipment 101 and only internal to thespecific operations of the third-party applications running thecomponents of the frontend infrastructure 102.

In other instances, the sensor data received from the frontendinfrastructure 102 may need conversion to other formats to present amore meaningful maintenance profile of the equipment 101 to its operatoron the UI client 114. For example, some implementations of the frontendinfrastructure 102 provide temperature readings in Celsius. For U.S.operators of equipment 101, the backend server 112 can convert thetemperature readings from Celsius to Fahrenheit.

FIG. 2 illustrates a system 200 for converting raw sensor data to aformat more useful for consumption by a human operator of the equipment101. Sensors 206 are similar in design and implementation to sensors106, as described above. Their sensor messages can come in a variety offormats, which may include superfluous data. A parsing module 205,implemented, for example, in the backend server 112, can scan the sensormessages and remove superfluous data. The clean sensor data can bestored in a database 216. The database 216 is similar in design andoperation to the database 116, described above. In some implementations,the type and identification of superfluous data can be pre-configured inthe parsing module 205, based on information on type and applicationsoftware implementing the frontend infrastructure 102. In otherembodiments, the parsing module 205 removes superfluous information bydetecting and filtering core sensor readings, e.g., temperature, time,equipment 101 identifier, etc.

UI clients 114 can also be used to receive from the operator of theequipment 101, an operation profile of the equipment 101. The operationprofile can include data, such as, device identifier of equipment 101,its location identifier, its brand, type, age, hours of operation,service and/or maintenance history, and a list of the physicalparameters, which the system 100 can monitor. Sensor data in combinationwith the operation profiles of a multitude of equipment devices 101across an industry can be used to generate insight into diagnostics,maintenance, and overall operation efficiency of the equipment devices101.

Range/Threshold-Based Notification

In some embodiments, the operation profile of the equipment 101 caninclude one or more threshold or ranges in which the equipment 101 isdeemed to function normally, and sensor readings outside those thresholdand ranges can indicate an equipment failure or a need for repair. Forexample, the sensors 106 can be deployed in walk-in coolers where atemperature range of 35-41 degrees Fahrenheit would indicate normaloperations and temperatures outside that range would indicate potentialproblem with the cooler. The operation profile of the equipment 101received at the backend server 112 can include data indicating thisrange.

The sensors 106 can continuously or near-continuously monitor, poll ordetect one or more physical parameters identified in the operationprofile of the equipment 101 and can periodically transmit the sensorreadings to gateway 108. The gateway 108 can receive and transmit thesensor reading data to the sensor-side server 110, as they are receivedor periodically at other predetermined intervals. The sensor-side server110 can transmit the received sensor readings to the backend server 112.

The sensors 106 can be deployed in a plurality and at various locationsand configuration with respect to equipment 101 in order to improve theoverall accuracy of the system 100. For example, in the context ofmonitoring walk-in coolers, sensors 106 can be deployed in a variety oflocations inside the walk-in cooler to provide a more thorough pictureof the temperature inside the cooler. For example, some sensors 106 canbe deployed near the door, some in the middle of the walk-in cooler andsome deeper inside the walk-in cooler. The sensor-side server 110 and/orthe backend server 112 can process the sensor data by assigning weightsto each sensor data based on sensor's location. In the example of awalk-in cooler, the sensor readings 106 from near the door of the coolercan be given a weight less than the sensors deep inside the walk-incooler, because temperatures near the door might typically be higherthan the temperatures inside the walk-in cooler. Therefore, highertemperature readings of that sensor are not necessarily indicative of anequipment failure. The operation profile of the equipment 101 caninclude sensor weight preferences of the operator of the equipment 101.

In some embodiments, the backend server 112 can store a history ofsensor readings and detect one or more patterns in the sensor readings.The patterns can indicate the weight each sensor reading should be givenin determining whether a maintenance notification alert should begenerated for the operator of the equipment 101. For example, ifhistorically temperature sensor readings from a sensor 106 have beenhigher than other sensor 106 readings, without the equipment 101 havingexperienced a failure, that can indicate that the higher temperaturesdetected in the location of the high temperature sensor 106 are normal.Perhaps that sensor 106 may be located near the door of a walk-incooler. As a result of detecting this historic pattern, the backendserver 112 can reduce the weight given to the high-temperature sensor106 in determining whether a maintenance alert should be generated forthe equipment 106.

In other words, the backend server 112 can detect one or more patternsin a plurality of sensor readings from a plurality of sensors 106,wherein the patterns indicate normal or abnormal equipment operationbased on the stored history of the sensor data. The same algorithm isapplicable to other types of sensors 106 and equipment 101. For example,a vibration sensor 106 reporting historically high vibrations, relativeto other vibration sensors 106 mounted on a manufacturing equipment 101,can indicate normal operations, if the manufacturing equipment 101 hasnot failed or reported a problem.

In some embodiments, the operation profile of the equipment 101 caninclude one or more instantaneous alert thresholds. While the frontendinfrastructure 102 sends sensor readings intermittently or atpre-determined intervals to the backend infrastructure 104 (e.g., every10 minutes or every 15 minutes), the sensors 106 can be configured totransmit some sensor readings instantly or near instantly. For example,an operation profile of a walk-in cooler can indicate that temperaturesfrom any sensor 106 above an instantaneous alert threshold (e.g., above55 degrees Fahrenheit) indicate an equipment failure. If sensors 106detect temperatures above the instantaneous alert threshold, they wakeup and transmit those temperatures instantly or near instantly,regardless of their normal transmission interval windows. Subsequently,the backend sever 112 can receive those sensor readings and generate analert for the UI clients 114.

Pattern-Based Notifications

In some embodiments, the sensor readings might not exceed a threshold orbe outside of a range indicated in the operation profile of equipment101. However, patterns and history of sensor readings can indicate atrajectory and approach, wherein it can be likely that the equipment 101might fail. For example, a vibration, current or temperature sensor 106can report readings that are gradually increasing over a period of timeand the trend in sensor reading indicates a near-future propensity toexceed a threshold or range indicated in the operation profile of theequipment 101. In other instances, the detected patterns can indicate anerratic range of values of the monitored parameters beyond the noiselevels of the system 100. For example, a temperature sensor 106 whosereported temperatures have been constant or near constant can startoscillating between a high temperature value and the constant value inperiodic or non-periodic manner. This can indicate an upcoming equipmentfailure. In other words, the present reported sensor values can becompared against stored sensor data of the same sensor to determinepatterns indicating abnormal or near-future abnormal state. The backendserver 112 can generate and transmit a repair notification to UI client114 associated with the equipment 101.

Additionally, the repair notification can include a course of actiondetermined based on the sensor readings, processed clean sensor data anddetected patterns in sensor data. For example, the backend server 112can detect a pattern of increasing temperatures in a sensor 106 locatednear the door of a walk-in cooler that persists throughout a 24-hourperiod. The door is expected to be shut for an extended period of timeduring night hours and the temperature readings near the door areexpected to drop during those hours. Other temperature sensors 106inside the walk-in cooler do not indicate the same pattern of risingtemperature and at night drop and stay constant. In this scenario, thetiming of sensor readings, the location of sensor readings, and thehistory of the sensor reading, can indicate a malfunction or broken sealin the door of the walk-in cooler. Consequently, the repair notificationcan include a course of action determined based on timing, location,involved parts, and corresponding solutions. For example, a notificationgenerated on the UI client 114 can include text phrase, “hightemperatures near cooler entrance during night. Inspect proper dooroperation and seal.”

In some embodiments, the backend server 112 can detect patternsindicative of normal or abnormal equipment operation, not only fromsensor data from an individual equipment 101, but from a collection ofequipment 101, that may be present at the same site or at differentlocations. In other words, a plurality of same or similar equipment 101can be included in the pattern detection of the backend server 112, forexample, sensor data from equipment 101 having same brand and/or typecan be included in pattern detection. In other examples, sensor data, aswell as notification data, stored in the database 116 over a period oftime can be used for pattern detection. Refrigerator XYZ brand of typeUVW can show a pattern of needing a condenser part replacement after10,000 hours of operations across various operators. Consequently,operators having the same or similar refrigerator can be sent a repairnotification at or before their equipment reaches 10,000 hours ofoperation.

In another embodiment, the detected patterns in sensor data from one ormore equipment 101 can be used to generate a set of conditions whoseexistence in another equipment 101 of same or similar type can indicatean anomaly. For example, when an increasing trend in temperature sensorreadings for a first equipment 101 is detected, the backend server 112can generate a set of conditions indicative of an anomaly, where the setof conditions are extracted from the first equipment 101. The set ofconditions can include data, such as brand, type, age, constituentparts, duration of time after which a part required maintenance, or anyother data correlated with the anomaly in the first equipment 101. Insome embodiments, maintenance records of the first equipment 101 storedon database 116 and/or received via operation profile of the firstequipment 101 can be used to further augment the set of conditionsindicative of an anomaly. In some embodiments, the set of conditionsindicative of an anomaly can be generated from a plurality of firstequipment 101.

Next, the backend server 112 can scan the received sensor readings froma second equipment 101, and if presence of the set of conditionsindicative of the anomaly is detected, the backend server 112 cangenerate a repair notification via UI clients 114. In some embodiments,the set of conditions can be used in a predictive manner, such that whenpresence of some of the set of conditions indicative of anomaly isdetected, and one or more remaining conditions are going to be satisfiedin the near future, the backend server 112, can issue a repairnotification. For example, when brand and type of a second equipment 101matches the brand and type in a set of condition indicative of anomaly,and the age of a part in the second equipment 101 is approaching an ageindicated in the set of conditions within a predetermined threshold, thebackend server 112 can generate a repair notification. The predeterminedthreshold can for example be encoded as within 90% of the value of aparameter in the set of conditions (e.g., hours of operation of thepart).

FIG. 3 illustrates a flow chart of a method 300 of providing automatedmaintenance using the systems 100 and 200. At step 302, the methodincludes receiving a profile of an equipment from an operator of theequipment, wherein the profile comprises one or more physical parametersof the equipment to be monitored and normal ranges of the physicalparameters. At step 304, the method includes monitoring, with one ormore sensors, the one or more physical parameters of the equipment. Atstep 306, the method includes transmitting the physical parameter valuesto a backend server. At step 308, the method includes determining if thephysical parameter values are outside the normal range and generating anotification. At step 310, the method includes determining one or morepatterns in the physical parameter values over a period of time. At step312, the method includes generating a notification if the one or morepatterns are indicative of an anomaly in operation of the equipment.

FIG. 4 illustrates a flowchart of a method 400, which can be used incombination with the embodiment of FIG. 3 to improve the operations ofthe system 100. At step 402, the method includes, generating equipmentrepair profiles of a plurality of equipment, corresponding to anomaliesdetected based on the one or more patterns in the physical parameters.At step 404, the method includes, detecting, based at least partly onthe equipment repair profiles, a set of conditions shared amongst theplurality of equipment having the detected anomalies. At step 406, themethod includes generating a second repair profile corresponding to theset of conditions and the detected anomalies corresponding to the set ofconditions. At step 408, the method includes detecting presence of theset of conditions in a second plurality of equipment. At step 410, themethod includes transmitting the second repair profile to one or moreoperators of the second plurality of equipment.

Artificial Intelligence (AI) Techniques of Detecting Patterns IndicatingDevice Failure

In some embodiments, AI techniques can be used to detect primary orsecondary patterns indicative of device or equipment failure. Primarypatterns can refer to patterns existing in raw or clean sensor 106 dataindicative of normal or abnormal device operation. Secondary patternscan refer to patterns existing in prior maintenance records of aplurality of equipment 101, which can be indicative of normal orabnormal device operations in same or similar equipment.

FIG. 5 illustrates a diagram 500 of an AI technique, which can be usedfor detecting primary or secondary patterns indicative of normal orabnormal device behavior. Sensor data of a first plurality of equipment101 along with their prior repair history of the plurality of theequipment 101 can be used to train multiple machine learning (ML)networks 506 (e.g., neural networks or deep neural networks) to detectpatterns of sensor data that can be correlated to normal or abnormaldevice operation. Each machine learning network 506 can be trained fordetecting patterns of sensor data correlated with specific malfunctionsin the first plurality of equipment 101. The past maintenance reportscan be used as ground truth and used to train the ML networks 506.Multiple instances of the ML networks 506 can be trained for variousbrands, types of equipment 101 or different parts within the equipment101. Furthermore, instances of the ML networks 506 can be trained todetect patterns of sensor data indicative of a malfunction, wherein eachinstance of ML network 506 is trained to detect patterns of sensor datacorrelated with a malfunction. After training, the ML networks 506 candetect patterns of sensor data correlated with one or more devicemalfunctions and can label the sensor data accordingly, therebygenerating labeled patterns of sensor data 508. The sensor data for thepurpose of training the ML networks 506 may be converted into variousdata structure formats, including, for example, vectors, arrays,multidimensional arrays, and/or tensors.

Next, sensor data 510 from a second plurality of equipment 101 whosemaintenance needs or repair history may be unknown are received by oneor more trained ML network 512. The ML networks 512 are the same orsimilar to the ML networks 506 after training. The trained ML networks512 can detect patterns of sensor data 508 in the input sensor data 510and label them accordingly. The labels can include information thatcorrelate with maintenance requirements of the second plurality ofequipment 101. For example, labels can include, “normal deviceoperation,” “abnormal device operation,” “part failure imminent,” orthey can include part specific information, such “part MNPQ imminentfailure,” or any label that may improve diagnostics and maintenance ofthe second plurality of equipment 101. At the same time, the backendserver maintains a mapping of which specific equipment 101, the sensordata containing a detected labeled pattern originate from (e.g, via adatabase entry mapping a device identifier with the stored sensorbarcode or QR code). The backend server 112 can therefore identify towhich equipment 101, the detected labeled pattern of sensor datacorresponds.

FIG. 6 illustrates a flowchart of a method 600 of using artificialintelligence (AI) techniques for detecting patterns of sensor dataindicative of device failure. At step 602, the method includes receivinga plurality of sensor data of a first plurality of equipment. At step604, the method includes receiving repair history associated with eachequipment of the first plurality of equipment. At step 606, the methodincludes training a plurality of machine learning networks to detectpatterns of sensor data indicative of a plurality of malfunctions,wherein each machine learning network is trained for detecting thepatterns of sensor data indicative of a single malfunction. At step 608,the method includes receiving sensor data of a second plurality ofequipment. At step 610, the method includes using one or more of thetrained plurality of machine learning networks to detect the patterns ofsensor data indicative of one or more malfunctions. At step 612, themethod includes identifying one or more equipment in the secondplurality of equipment having sensor data, comprising the detectedpatterns of sensor data. At step 614, the method includes, generatingand transmitting a notification to an operator of the one or moreequipment in the second plurality of equipment, wherein the notificationcomprises a notification of the one or more malfunction.

The described embodiments of the backend infrastructure can beimplemented using a network of computers and processors, including forexample, Amazon Web services (AWS). In one embodiment, the backendserver 112 can be a Node.js server. In some embodiments, a computerstorage medium can include instructions that when executed cause one ormore computer processors to perform operations, including thosedescribed in methods and techniques outlined above.

What is claimed is:
 1. A method comprising: receiving a profile of anequipment from an operator of the equipment, wherein the profilecomprises one or more physical parameters of the equipment to bemonitored a device identifier of the equipment to be monitored, alocation of the equipment to be monitored, sensor weight preferences andnormal ranges of the physical parameters; monitoring, with one or moresensors, the one or more physical parameters of the equipment;transmitting, by the one or more sensors, the physical parameters via agateway to a sensor-side server; transmitting, by the sensor-sideserver, sensor data including the physical parameter values andsuperfluous data to a backend server, the superfluous data including atleast one or more of extra characters, encoding data, third-partyapplication data and device identifiers unrelated to the equipment; atthe backend server, parsing the sensor data to remove the superfluousdata from the sensor data and retaining the physical parameter values;weighting the physical parameter values based on a location of thesensor that obtained the physical parameter values; determining if theweighted physical parameter values are outside the normal range as setforth in the profile of the equipment and generating a notification;determining one or more patterns in the physical parameter values over aperiod of time; and generating a notification if the one or morepatterns are indicative of an anomaly in operation of the equipment. 2.The method of claim 1, wherein the one or more patterns indicative of ananomaly comprise the one or more parameter values approaching a rangeoutside the normal range over a period of time, but not exceeding thenormal range over the period of time.
 3. The method of claim 1, whereinthe one or more patterns indicative of an anomaly are determined via oneor more machine learning algorithms based on monitored parameter valuesof a plurality of equipment over a period of time.
 4. The method ofclaim 1 further comprising: detecting one or more equipment-widepatterns indicative of anomaly in operation, the one or moreequipment-wide patterns shared among a plurality of same or similarequipment; and generating a notification for some or all of the same orsimilar equipment.
 5. The method of claim 1 further comprising:generating a set of conditions based at least partly on the one or morepatterns, wherein the set of conditions correlate with the anomaly;generating an equipment repair profile corresponding to the anomaly,along with a mapping of the equipment to the equipment repair profile;detecting the presence of the set of conditions in a second equipment;and transmitting the equipment repair profile to an operator of thesecond equipment.
 6. The method of claim 5 further comprisingtransmitting the equipment repair profile to other operators havingequipment same or similar to the second equipment.
 7. The method ofclaim 1, further comprising: generating equipment repair profiles of aplurality of equipment, corresponding to the anomaly detected based onthe one or more patterns in the physical parameters; based at leastpartly on the equipment repair profiles, detecting a set of conditionsshared amongst the plurality of equipment having the detected anomaly;generating a second repair profile corresponding to the set ofconditions and the detected anomaly corresponding to the set ofconditions; detecting presence of the set of conditions in a secondplurality of equipment; and transmitting the second repair profile toone or more operators of the second plurality of equipment.
 8. Themethod of claim 7, wherein the set of conditions comprise one or moreof; a brand of the equipment, or part identification of a previouslyrepaired or replaced part, and a duration of time after which the partneeded repair or replacement.
 9. The method of claim 1, wherein thephysical parameters comprise one or more of temperature, vibration,electrical current and power drawn.
 10. The method of claim 1, furthercomprising: transmitting a sensor configuration signal from the backendserver to the one or more sensors, wherein the sensor configurationsignal is at least partly based on the equipment profile. 11.Non-transitory computer storage that stores executable programinstructions that, when executed by one or more computing devices,configure the one or more computing devices to perform operationscomprising: receiving a profile of an equipment from an operator of theequipment, wherein the profile comprises one or more physical parametersof the equipment to be monitored, a device identifier of the equipmentto be monitored, a location of the equipment to be monitored, sensorweight preferences and normal ranges of the physical parameters;monitoring, with one or more sensors, the one or more physicalparameters of the equipment; transmitting, by the one or more sensors,the physical parameters via a gateway to a sensor-side server;transmitting, by the sensor-side server, sensor data including thephysical parameter values and superfluous data to a backend server, thesuperfluous data including at least one or more of extra characters,encoding data, third-party application data and device identifiersunrelated to the equipment; at the backend server, parsing the sensordata to remove the superfluous data from the sensor data and retain thephysical parameter values; determining weighted physical parametervalues by weighting the physical parameter values based on a location ofthe sensor that obtained the physical parameter values; determining ifthe weighted physical parameter values are outside the normal range asset forth in the profile of the equipment and generating a notification;determining one or more patterns in the physical parameter values over aperiod of time; and generating a notification if the one or morepatterns are indicative of an anomaly in operation of the equipment. 12.The non-transitory storage of claim 11, wherein the one or more patternsindicative of an anomaly comprise the one or more parameter valuesapproaching a range outside the normal range over a period of time, butnot exceeding the normal range over the period of time.
 13. Thenon-transitory storage of claim 11, wherein the one or more patternsindicative of an anomaly are determined via one or more machine learningalgorithms based on monitored parameter values of a plurality ofequipment over a period of time.
 14. The non-transitory storage of claim11, wherein the operations further comprise: detecting one or moreequipment-wide patterns indicative of anomaly in operation, the one ormore equipment-wide patterns shared among a plurality of same or similarequipment; and generating a notification for some or all of the same orsimilar equipment.
 15. The non-transitory storage of claim 11, whereinthe operations further comprise: generating a set of conditions based atleast partly on the one or more patterns, wherein the set of conditionscorrelate with the anomaly; generating an equipment repair profilecorresponding to the anomaly, along with a mapping of the equipment tothe equipment repair profile; detecting the presence of the set ofconditions in a second equipment; and transmitting the equipment repairprofile to an operator of the second equipment.
 16. The non-transitorystorage of claim 15, wherein the operations further comprise:transmitting the equipment repair profile to other operators havingequipment same or similar to the second equipment.
 17. Thenon-transitory storage of claim 11, wherein the operations furthercomprise: generating equipment repair profiles of a plurality ofequipment, corresponding to the anomaly detected based on the one ormore patterns in the physical parameters; based at least partly on theequipment repair profiles, detecting a set of conditions shared amongstthe plurality of equipment having the detected anomaly; generating asecond repair profile corresponding to the set of conditions and thedetected anomaly corresponding to the set of conditions; detectingpresence of the set of conditions in a second plurality of equipment;and transmitting the second repair profile to one or more operators ofthe second plurality of equipment.
 18. The non-transitory storage ofclaim 17, wherein the set of conditions comprise one or more of a brandof the equipment, or part identification of a previously repaired orreplaced part, and a duration of time after which the part needed repairor replacement.
 19. The non-transitory storage of claim 11, wherein thephysical parameters comprise one or more of temperature, vibration,electrical current and power drawn.
 20. The non-transitory storage ofclaim 11, wherein the operations further comprise: transmitting a sensorconfiguration signal from the backend server to the one or more sensors,wherein the sensor configuration signal is at least partly based on theequipment profile.